Comprehensive evaluation of triglyceride glucose index–a body shape index (TyG–ABSI) for incident peripheral artery disease: data-driven phenotyping and machine learning-based risk prediction in the UK Biobank
TyG-ABSI is a robust, independent predictor of long-term PAD risk. Data-driven phenotyping and interpretable machine learning facilitate more precise risk stratification. Logistic regression offers optimal performance and interpretability, holding potential clinical utility for individualized PAD risk prediction.
Authors
- Wei Zhao (ORCID: https://orcid.org/0000-0002-6268-2559)
- Xiaolu Li
- Zuoguan G. Chen
- Xihao Zhang (ORCID: https://orcid.org/0000-0003-4786-3228)
- Chengran Lu
- Bo Zhang (ORCID: https://orcid.org/0000-0002-0254-5843)
- Yong Lan
- Yongpeng Diao
- Zhiyuan Wu
- Yaming Guo
- Yongjun Li
- Yifan Cao
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking University (CN)
- Peking Union Medical College Hospital (CN)
- Peking University People's Hospital (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Cardiovascular Diabetology
- Published
- 2026-03-09
- DOI
- https://doi.org/10.1186/s12933-026-03116-0
- Primary Topic
- Peripheral Artery Disease Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00